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SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings
April 30, 2024, 4:42 a.m. | Kang Liu
cs.LG updates on arXiv.org arxiv.org
Abstract: Taking inspiration from Set Theory, we introduce SetCSE, an innovative information retrieval framework. SetCSE employs sets to represent complex semantics and incorporates well-defined operations for structured information querying under the provided context. Within this framework, we introduce an inter-set contrastive learning objective to enhance comprehension of sentence embedding models concerning the given semantics. Furthermore, we present a suite of operations, including SetCSE intersection, difference, and operation series, that leverage sentence embeddings of the enhanced model …
abstract arxiv context cs.ir cs.lg embeddings framework information inspiration operations retrieval semantics set theory type
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